Impact of Legal Requirements on Explainability in Machine Learning

Adrien Bibal; Michaël Lognoul; Alexandre De Streel; Benoît Frénay

Impact of Legal Requirements on Explainability in Machine Learning

Adrien Bibal, Michaël Lognoul, Alexandre De Streel, Benoît Frénay

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

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Abstract

The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

Original language	English
Title of host publication	Proceedings of the 37th International Conference on Machine Learning
Publisher	MLResearch Press
Number of pages	2
Publication status	Published - 2020
Event	Thirty-seventh International Conference on Machine Learning: ICML2020 - Duration: 13 Jul 2020 → 18 Jul 2020

Publication series

Name	Proceedings of Machine Learning Research
Publisher	MLResearch Press
Volume	108
ISSN (Electronic)	2640-3498

Conference

Conference	Thirty-seventh International Conference on Machine Learning
Period	13/07/20 → 18/07/20

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Abstract_LML_Workshop_Explainability_AI_LawAccepted author manuscript, 92.4 KB

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Bibal, A. (Author)FRENAY, B. (Supervisor), VANHOOF, W. (President), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Jury) & Galarraga, L. (Jury), 16 Nov 2020
Student thesis: Doc types › Doctor of Sciences

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@inproceedings{4c13974296c547278b93c7c46b0cda40,

title = "Impact of Legal Requirements on Explainability in Machine Learning",

abstract = "The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques. ",

author = "Adrien Bibal and Micha{\"e}l Lognoul and {De Streel}, Alexandre and Beno{\^i}t Fr{\'e}nay",

year = "2020",

language = "English",

series = "Proceedings of Machine Learning Research",

publisher = "MLResearch Press",

booktitle = "Proceedings of the 37th International Conference on Machine Learning",

note = "Thirty-seventh International Conference on Machine Learning : ICML2020 ; Conference date: 13-07-2020 Through 18-07-2020",

}

Impact of Legal Requirements on Explainability in Machine Learning. / Bibal, Adrien ; Lognoul, Michaël ; De Streel, Alexandre et al.
Proceedings of the 37th International Conference on Machine Learning. MLResearch Press, 2020. (Proceedings of Machine Learning Research; Vol. 108).

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Impact of Legal Requirements on Explainability in Machine Learning

AU - Bibal, Adrien

AU - Lognoul, Michaël

AU - De Streel, Alexandre

AU - Frénay, Benoît

PY - 2020

Y1 - 2020

N2 - The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

AB - The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

M3 - Conference contribution

T3 - Proceedings of Machine Learning Research

BT - Proceedings of the 37th International Conference on Machine Learning

PB - MLResearch Press

T2 - Thirty-seventh International Conference on Machine Learning

Y2 - 13 July 2020 through 18 July 2020

ER -

Impact of Legal Requirements on Explainability in Machine Learning

Abstract

Publication series

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Student theses

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction

Cite this